class_docstr: Augmenter that leverage semantic meaning to substitute word.
  
  :param str lang: Language of your text. Default value is 'eng'.
  :param float aug_p: Percentage of word will be augmented.
  :param int aug_min: Minimum number of word will be augmented.
  :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from
      aug_p. Otherwise, using aug_max.
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.AntonymAug()
code: def get_model(cls, aug_src, lang):
      if aug_src == "wordnet":
          return nmw.WordNet(lang=lang, is_synonym=False)
  
file_path: nlpaug/nlpaug/augmenter/word/antonym.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc, PartOfSpeech, WarningException, WarningName, WarningCode, WarningMessage
  import nlpaug.model.word_dict
label_desc: Use image node_id 6 for calling the AntonymAug obj's underlying member method code with example usage: obj.get_model(cls, aug_src, lang) and returns: nmw